In order to make better decisions, faster, users want to be able to do ad-hoc visual analytics and to explore and apply dynamic analytics on historical and real-time data sets without having to do lengthy preparation or modeling of the data up-front and without having in-depth knowledge of the data. Prior solutions require some combination of: up-front modeling of data, detailed knowledge of the data, up-front-data modeling and/or deep technical understanding of the various analytics operations.
In a conventional process illustrated, for example, in FIG. 12, a data source 1201 provides data 1203 which has an a priori known data format, such as from a stock market. A process to generate conventional analytics 1221 has already input 1223 a defined model for the data, such as a model for stock market data. The process then runs 1225 the data into the pre-determined model which is known to be appropriate for the data. A user manually prepares queries 1227 which can be run on data in the pre-determined model. The queries are run, and the query results are displayed 1229 to the user.